Hyperparameter Optimization Techniques to Improve Your Machine Learning Model's Performance

When working on a machine learning project, you need to follow a series of steps until you reach your goal. One of the steps you have to perform is hyperparameter optimization on your selected model. This task always comes after the model selection process where you choose the model that is performing better than other models. Before I define hyperparameter optimization, you need to understand what a hyperparameter is. In short, hyperparameters are different parameter values that are used to control the learning process and have a significant effect on the performance of machine learning models. An example of hyperparameters in the Random Forest algorithm is the number of estimators (n_estimators), maximum depth (max_depth), and criterion. These parameters are tunable and can directly affect how well a model trains. So then hyperparameter optimization is the process of finding the right combination of hyperparameter values to achieve maximum performance on the data in a reasonable amount of time.